Overview

Dataset statistics

Number of variables52
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)4.4%
Total size in memory30.2 KiB
Average record size in memory150.6 B

Variable types

Numeric14
Boolean38

Alerts

Dataset has 9 (4.4%) duplicate rowsDuplicates
symboling is highly overall correlated with wheelbase and 3 other fieldsHigh correlation
wheelbase is highly overall correlated with symboling and 14 other fieldsHigh correlation
carlength is highly overall correlated with wheelbase and 17 other fieldsHigh correlation
carwidth is highly overall correlated with wheelbase and 16 other fieldsHigh correlation
carheight is highly overall correlated with symboling and 11 other fieldsHigh correlation
curbweight is highly overall correlated with wheelbase and 14 other fieldsHigh correlation
enginesize is highly overall correlated with wheelbase and 20 other fieldsHigh correlation
boreratio is highly overall correlated with wheelbase and 11 other fieldsHigh correlation
stroke is highly overall correlated with enginelocation_front and 3 other fieldsHigh correlation
compressionratio is highly overall correlated with fueltype_diesel and 6 other fieldsHigh correlation
horsepower is highly overall correlated with wheelbase and 19 other fieldsHigh correlation
peakrpm is highly overall correlated with fueltype_diesel and 5 other fieldsHigh correlation
citympg is highly overall correlated with carlength and 12 other fieldsHigh correlation
highwaympg is highly overall correlated with wheelbase and 15 other fieldsHigh correlation
fueltype_diesel is highly overall correlated with compressionratio and 3 other fieldsHigh correlation
fueltype_gas is highly overall correlated with compressionratio and 3 other fieldsHigh correlation
aspiration_std is highly overall correlated with compressionratio and 1 other fieldsHigh correlation
aspiration_turbo is highly overall correlated with compressionratio and 1 other fieldsHigh correlation
doornumber_four is highly overall correlated with symboling and 4 other fieldsHigh correlation
doornumber_two is highly overall correlated with symboling and 4 other fieldsHigh correlation
carbody_hatchback is highly overall correlated with carheight and 3 other fieldsHigh correlation
carbody_sedan is highly overall correlated with doornumber_four and 2 other fieldsHigh correlation
carbody_wagon is highly overall correlated with carheightHigh correlation
drivewheel_fwd is highly overall correlated with wheelbase and 9 other fieldsHigh correlation
drivewheel_rwd is highly overall correlated with wheelbase and 9 other fieldsHigh correlation
enginelocation_front is highly overall correlated with wheelbase and 4 other fieldsHigh correlation
enginelocation_rear is highly overall correlated with wheelbase and 4 other fieldsHigh correlation
enginetype_dohcv is highly overall correlated with boreratio and 1 other fieldsHigh correlation
enginetype_l is highly overall correlated with wheelbase and 4 other fieldsHigh correlation
enginetype_ohc is highly overall correlated with carlength and 1 other fieldsHigh correlation
enginetype_ohcf is highly overall correlated with strokeHigh correlation
enginetype_ohcv is highly overall correlated with enginesize and 2 other fieldsHigh correlation
enginetype_rotor is highly overall correlated with carheight and 4 other fieldsHigh correlation
cylindernumber_eight is highly overall correlated with carlength and 5 other fieldsHigh correlation
cylindernumber_five is highly overall correlated with carwidthHigh correlation
cylindernumber_four is highly overall correlated with carwidth and 6 other fieldsHigh correlation
cylindernumber_six is highly overall correlated with enginesize and 2 other fieldsHigh correlation
cylindernumber_three is highly overall correlated with carlength and 4 other fieldsHigh correlation
cylindernumber_twelve is highly overall correlated with carheight and 3 other fieldsHigh correlation
cylindernumber_two is highly overall correlated with carheight and 4 other fieldsHigh correlation
fuelsystem_1bbl is highly overall correlated with carlength and 2 other fieldsHigh correlation
fuelsystem_2bbl is highly overall correlated with carlength and 6 other fieldsHigh correlation
fuelsystem_4bbl is highly overall correlated with carheight and 3 other fieldsHigh correlation
fuelsystem_idi is highly overall correlated with compressionratio and 3 other fieldsHigh correlation
fuelsystem_mpfi is highly overall correlated with wheelbase and 8 other fieldsHigh correlation
fueltype_diesel is highly imbalanced (53.9%)Imbalance
fueltype_gas is highly imbalanced (53.9%)Imbalance
carbody_convertible is highly imbalanced (80.9%)Imbalance
carbody_hardtop is highly imbalanced (76.2%)Imbalance
drivewheel_4wd is highly imbalanced (74.0%)Imbalance
enginelocation_front is highly imbalanced (89.0%)Imbalance
enginelocation_rear is highly imbalanced (89.0%)Imbalance
enginetype_dohc is highly imbalanced (67.8%)Imbalance
enginetype_dohcv is highly imbalanced (95.6%)Imbalance
enginetype_l is highly imbalanced (67.8%)Imbalance
enginetype_ohcf is highly imbalanced (62.2%)Imbalance
enginetype_ohcv is highly imbalanced (65.9%)Imbalance
enginetype_rotor is highly imbalanced (86.1%)Imbalance
cylindernumber_eight is highly imbalanced (83.5%)Imbalance
cylindernumber_five is highly imbalanced (69.8%)Imbalance
cylindernumber_three is highly imbalanced (95.6%)Imbalance
cylindernumber_twelve is highly imbalanced (95.6%)Imbalance
cylindernumber_two is highly imbalanced (86.1%)Imbalance
fuelsystem_1bbl is highly imbalanced (69.8%)Imbalance
fuelsystem_4bbl is highly imbalanced (89.0%)Imbalance
fuelsystem_idi is highly imbalanced (53.9%)Imbalance
fuelsystem_mfi is highly imbalanced (95.6%)Imbalance
fuelsystem_spdi is highly imbalanced (74.0%)Imbalance
fuelsystem_spfi is highly imbalanced (95.6%)Imbalance
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-08-30 04:37:30.813775
Analysis finished2023-08-30 04:37:53.873506
Duration23.06 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-08-29T23:37:53.935520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-08-29T23:37:54.042545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

wheelbase
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:54.168572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2023-08-29T23:37:54.312604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

carlength
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:54.453631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2023-08-29T23:37:54.604490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

carwidth
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:54.749522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2023-08-29T23:37:54.895631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

carheight
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:55.037641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2023-08-29T23:37:55.188691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

curbweight
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:55.332614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2023-08-29T23:37:55.481634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

enginesize
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:55.688892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2023-08-29T23:37:55.827949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%

boreratio
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:55.955981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2023-08-29T23:37:56.092026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

stroke
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:56.227091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2023-08-29T23:37:56.353011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

compressionratio
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:56.473324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-08-29T23:37:56.597352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

horsepower
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:56.733382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2023-08-29T23:37:56.873414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

peakrpm
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:56.992440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2023-08-29T23:37:57.108465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

citympg
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:57.223492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2023-08-29T23:37:57.351520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highwaympg
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-29T23:37:57.475548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2023-08-29T23:37:57.608577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

fueltype_diesel
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
185 
True
20 
ValueCountFrequency (%)
False 185
90.2%
True 20
 
9.8%
2023-08-29T23:37:57.793619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

fueltype_gas
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
185 
False
20 
ValueCountFrequency (%)
True 185
90.2%
False 20
 
9.8%
2023-08-29T23:37:57.887682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
168 
False
37 
ValueCountFrequency (%)
True 168
82.0%
False 37
 
18.0%
2023-08-29T23:37:57.987704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
168 
True
37 
ValueCountFrequency (%)
False 168
82.0%
True 37
 
18.0%
2023-08-29T23:37:58.081726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
115 
False
90 
ValueCountFrequency (%)
True 115
56.1%
False 90
43.9%
2023-08-29T23:37:58.177747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
115 
True
90 
ValueCountFrequency (%)
False 115
56.1%
True 90
43.9%
2023-08-29T23:37:58.272768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
199 
True
 
6
ValueCountFrequency (%)
False 199
97.1%
True 6
 
2.9%
2023-08-29T23:37:58.368790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
197 
True
 
8
ValueCountFrequency (%)
False 197
96.1%
True 8
 
3.9%
2023-08-29T23:37:58.460811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
135 
True
70 
ValueCountFrequency (%)
False 135
65.9%
True 70
34.1%
2023-08-29T23:37:58.555832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
109 
True
96 
ValueCountFrequency (%)
False 109
53.2%
True 96
46.8%
2023-08-29T23:37:58.649853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
180 
True
25 
ValueCountFrequency (%)
False 180
87.8%
True 25
 
12.2%
2023-08-29T23:37:58.746874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
196 
True
 
9
ValueCountFrequency (%)
False 196
95.6%
True 9
 
4.4%
2023-08-29T23:37:58.840895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
120 
False
85 
ValueCountFrequency (%)
True 120
58.5%
False 85
41.5%
2023-08-29T23:37:58.934916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
129 
True
76 
ValueCountFrequency (%)
False 129
62.9%
True 76
37.1%
2023-08-29T23:37:59.029937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginelocation_front
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
202 
False
 
3
ValueCountFrequency (%)
True 202
98.5%
False 3
 
1.5%
2023-08-29T23:37:59.123958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginelocation_rear
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
202 
True
 
3
ValueCountFrequency (%)
False 202
98.5%
True 3
 
1.5%
2023-08-29T23:37:59.217585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
193 
True
 
12
ValueCountFrequency (%)
False 193
94.1%
True 12
 
5.9%
2023-08-29T23:37:59.309605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginetype_dohcv
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
204 
True
 
1
ValueCountFrequency (%)
False 204
99.5%
True 1
 
0.5%
2023-08-29T23:37:59.403626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginetype_l
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
193 
True
 
12
ValueCountFrequency (%)
False 193
94.1%
True 12
 
5.9%
2023-08-29T23:37:59.495646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
148 
False
57 
ValueCountFrequency (%)
True 148
72.2%
False 57
 
27.8%
2023-08-29T23:37:59.589667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginetype_ohcf
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
190 
True
 
15
ValueCountFrequency (%)
False 190
92.7%
True 15
 
7.3%
2023-08-29T23:37:59.683688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginetype_ohcv
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
192 
True
 
13
ValueCountFrequency (%)
False 192
93.7%
True 13
 
6.3%
2023-08-29T23:37:59.776709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

enginetype_rotor
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
201 
True
 
4
ValueCountFrequency (%)
False 201
98.0%
True 4
 
2.0%
2023-08-29T23:37:59.867729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

cylindernumber_eight
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
200 
True
 
5
ValueCountFrequency (%)
False 200
97.6%
True 5
 
2.4%
2023-08-29T23:37:59.960750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

cylindernumber_five
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
194 
True
 
11
ValueCountFrequency (%)
False 194
94.6%
True 11
 
5.4%
2023-08-29T23:38:00.056664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
True
159 
False
46 
ValueCountFrequency (%)
True 159
77.6%
False 46
 
22.4%
2023-08-29T23:38:00.153686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
181 
True
24 
ValueCountFrequency (%)
False 181
88.3%
True 24
 
11.7%
2023-08-29T23:38:00.248708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

cylindernumber_three
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
204 
True
 
1
ValueCountFrequency (%)
False 204
99.5%
True 1
 
0.5%
2023-08-29T23:38:00.343729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

cylindernumber_twelve
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
204 
True
 
1
ValueCountFrequency (%)
False 204
99.5%
True 1
 
0.5%
2023-08-29T23:38:00.497763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

cylindernumber_two
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
201 
True
 
4
ValueCountFrequency (%)
False 201
98.0%
True 4
 
2.0%
2023-08-29T23:38:00.588950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

fuelsystem_1bbl
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
194 
True
 
11
ValueCountFrequency (%)
False 194
94.6%
True 11
 
5.4%
2023-08-29T23:38:00.681970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
139 
True
66 
ValueCountFrequency (%)
False 139
67.8%
True 66
32.2%
2023-08-29T23:38:00.773991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

fuelsystem_4bbl
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
202 
True
 
3
ValueCountFrequency (%)
False 202
98.5%
True 3
 
1.5%
2023-08-29T23:38:00.869013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

fuelsystem_idi
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
185 
True
20 
ValueCountFrequency (%)
False 185
90.2%
True 20
 
9.8%
2023-08-29T23:38:00.963033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
204 
True
 
1
ValueCountFrequency (%)
False 204
99.5%
True 1
 
0.5%
2023-08-29T23:38:01.058054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
111 
True
94 
ValueCountFrequency (%)
False 111
54.1%
True 94
45.9%
2023-08-29T23:38:01.160077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
196 
True
 
9
ValueCountFrequency (%)
False 196
95.6%
True 9
 
4.4%
2023-08-29T23:38:01.256098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size333.0 B
False
204 
True
 
1
ValueCountFrequency (%)
False 204
99.5%
True 1
 
0.5%
2023-08-29T23:38:01.354120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-08-29T23:37:51.806302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:35.576876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:39.395165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.688014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.901411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.142973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.329561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.634807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.792065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.110794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.313873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:50.567180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:51.962337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:35.663786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:36.912457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.229895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:39.478184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.771032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.985430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.224000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.415473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.714824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.878088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.192812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.394892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:50.653200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.056359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:35.752797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:37.003478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.322916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:39.590282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.862053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:42.073449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.311036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.509494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.800843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.033553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.279831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.543930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:50.747220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.149379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:35.843821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:37.096498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.413936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:39.680229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.953073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:42.164470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.401097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.602573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.887863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.132575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.371663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.634950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:50.839242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.238399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:41.040092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:44.753605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.969882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.226596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.456682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.719969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:50.929066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.329420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:36.058596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:37.277539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.593976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:39.853268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.127112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:42.333508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.569163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.840624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.051899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.315616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.543701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:42.411526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.650170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.927644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.131917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.400635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.624719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.899009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:51.105147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.502718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:36.224636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:37.450721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.765025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.020306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.295284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:42.552557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.731188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.011662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.211935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.487654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.705738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:49.979027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:51.190165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.598739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:36.313657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:37.546743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.857046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.112801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.386305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:42.639576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.820241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.102683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.297955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.580675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.794757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:50.067047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:51.282186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.682758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:36.392675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:37.629761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:38.943065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:40.192818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.466547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:42.718594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:43.899376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.184701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.373972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:47.663694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:50.146064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:36.480694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:40.343852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:41.555598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:43.987307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:47.753713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:48.962795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:51.455224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:52.862798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:42.886706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:44.067324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:45.363741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:46.539009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-08-29T23:37:50.474160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-29T23:37:51.713282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-29T23:38:01.490150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
symbolingwheelbasecarlengthcarwidthcarheightcurbweightenginesizeboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgfueltype_dieselfueltype_gasaspiration_stdaspiration_turbodoornumber_fourdoornumber_twocarbody_convertiblecarbody_hardtopcarbody_hatchbackcarbody_sedancarbody_wagondrivewheel_4wddrivewheel_fwddrivewheel_rwdenginelocation_frontenginelocation_rearenginetype_dohcenginetype_dohcvenginetype_lenginetype_ohcenginetype_ohcfenginetype_ohcvenginetype_rotorcylindernumber_eightcylindernumber_fivecylindernumber_fourcylindernumber_sixcylindernumber_threecylindernumber_twelvecylindernumber_twofuelsystem_1bblfuelsystem_2bblfuelsystem_4bblfuelsystem_idifuelsystem_mfifuelsystem_mpfifuelsystem_spdifuelsystem_spfi
symboling1.000-0.538-0.396-0.254-0.523-0.256-0.177-0.170-0.0190.023-0.0100.282-0.0180.0530.2170.2170.1850.1850.6840.6840.3320.1540.4600.3970.3150.1710.3240.3310.2720.2720.1620.0000.2760.3330.1670.0000.3270.0000.0000.2300.2030.0450.0000.3270.0000.4200.2720.2170.0890.2830.2490.045
wheelbase-0.5381.0000.9120.8120.6330.7650.6480.5370.227-0.1260.505-0.312-0.493-0.5390.3410.3410.3100.3100.4450.4450.3630.2500.4010.3330.3130.1680.5150.5770.5680.5680.3310.0000.6570.4220.3290.3380.0000.4970.2750.4270.4490.2770.0000.0000.2030.4470.0000.3410.0000.5300.1120.000
carlength-0.3960.9121.0000.8880.5250.8900.7830.6390.187-0.1930.661-0.269-0.670-0.6980.1100.1100.2070.2070.3650.3650.0000.0000.4390.3210.3120.0350.5530.5700.0000.0000.3200.0000.5390.5290.2910.2810.0940.5030.3120.4190.2790.5360.0350.0940.6070.5810.0000.1100.0000.5690.1670.000
carwidth-0.2540.8120.8881.0000.3500.8640.7710.6100.240-0.1460.689-0.199-0.688-0.7010.2330.2330.3010.3010.3050.3050.1850.0000.1910.1970.0000.0000.5450.5860.1600.1600.2020.2770.7210.4120.1680.3270.1330.6140.5040.5690.3500.9800.3450.1330.1720.5620.0440.2330.0000.5110.1900.000
carheight-0.5230.6330.5250.3501.0000.3460.2000.216-0.0180.0000.011-0.296-0.069-0.1330.2770.2770.2370.2370.5410.5410.4220.2600.5460.4940.6820.2020.4480.4650.2720.2720.2970.0000.5270.3920.2340.2830.6250.1360.0000.3070.2000.0000.5360.6250.1730.3470.5300.2770.0000.3730.2880.000
curbweight-0.2560.7650.8900.8640.3461.0000.8780.7020.163-0.2190.808-0.236-0.813-0.8340.3050.3050.3750.3750.2740.2740.1510.3030.2890.1820.1710.0000.6430.6470.1000.1000.2810.2770.4530.4050.1110.4720.1330.6150.2870.5800.4330.6750.4510.1330.2100.6170.0440.3050.0000.5810.1610.000
enginesize-0.1770.6480.7830.7710.2000.8781.0000.7010.292-0.2350.817-0.273-0.730-0.7210.1570.1570.2710.2710.2070.2070.2530.2970.2130.0750.0000.0000.6080.6890.6190.6190.4130.3160.2380.4690.2890.7730.7900.8070.3420.8000.7490.3520.5410.7900.1460.4710.6750.1570.0000.5050.0860.000
boreratio-0.1700.5370.6390.6100.2160.7020.7011.000-0.083-0.1600.639-0.298-0.609-0.6150.1680.1680.3350.3350.1630.1630.0710.1860.3110.1340.0000.0880.5780.6040.3270.3270.0920.6790.3330.4530.4040.3930.3130.3350.2300.2260.2610.2860.0000.3130.7290.4400.2520.1680.0000.4620.0000.000
stroke-0.0190.2270.1870.240-0.0180.1630.292-0.0831.000-0.0700.130-0.074-0.030-0.0300.3750.3750.2650.2650.1320.1320.1340.1780.2470.1360.0000.3940.2440.2950.6150.6150.4040.0000.2650.5130.8450.0970.1010.0370.3200.2930.3830.0000.3080.1010.2980.4040.0000.3750.3080.2610.4640.000
compressionratio0.023-0.126-0.193-0.1460.000-0.219-0.235-0.160-0.0701.000-0.353-0.0220.4790.4450.9930.9930.5540.5540.1860.1860.0000.0000.1730.1760.0300.0000.1750.1900.0000.0000.0000.0000.6480.2920.0390.2510.0000.1250.3330.1630.0000.0000.9930.0000.1210.2520.0000.9930.0000.2970.3210.000
horsepower-0.0100.5050.6610.6890.0110.8080.8170.6390.130-0.3531.0000.113-0.911-0.8860.2190.2190.3430.3430.1710.1710.1240.3220.1510.1590.0000.0000.5610.5920.8430.8430.3320.9830.1010.4390.4100.5230.0900.5290.3960.7070.6600.0000.9830.0900.2590.6670.0830.2190.0000.6640.2330.000
peakrpm0.282-0.312-0.269-0.199-0.296-0.236-0.273-0.298-0.074-0.0220.1131.000-0.131-0.0570.5940.5940.3110.3110.2440.2440.0000.1300.0970.2000.0000.1940.2600.2890.4480.4480.3850.2860.3570.3040.2750.2030.5310.2520.2950.2520.2600.0000.0000.5310.5500.3260.4480.5940.0000.2050.1680.000
citympg-0.018-0.493-0.670-0.688-0.069-0.813-0.730-0.609-0.0300.479-0.911-0.1311.0000.9680.3890.3890.1860.1860.0030.0030.0000.0000.1540.0950.0000.0000.5390.5180.1100.1100.1360.0000.1900.4360.0000.4230.1100.4630.2270.6920.4460.6790.2000.1100.1960.6060.1100.3890.0000.6590.0850.000
highwaympg0.053-0.539-0.698-0.701-0.133-0.834-0.721-0.615-0.0300.445-0.886-0.0570.9681.0000.3360.3360.3190.3190.1190.1190.0000.0000.1240.0940.0000.0910.6110.5870.1010.1010.2850.0000.2960.4970.0000.5390.4210.6370.3000.6990.4150.6750.3080.4210.3470.6400.3470.3360.0000.6330.1190.000
fueltype_diesel0.2170.3410.1100.2330.2770.3050.1570.1680.3750.9930.2190.5940.3890.3361.0000.9720.3740.3740.1610.1610.0000.0000.1710.1540.0000.0000.0230.0780.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.1970.0000.9720.0000.2780.0000.000
fueltype_gas0.2170.3410.1100.2330.2770.3050.1570.1680.3750.9930.2190.5940.3890.3360.9721.0000.3740.3740.1610.1610.0000.0000.1710.1540.0000.0000.0230.0780.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.1970.0000.9720.0000.2780.0000.000
aspiration_std0.1850.3100.2070.3010.2370.3750.2710.3350.2650.5540.3430.3110.1860.3190.3740.3741.0000.9830.0000.0000.0000.0000.0000.0000.0000.0000.1130.0710.0000.0000.0000.0000.1660.0000.0000.0000.0000.0000.1850.0000.0180.0000.0000.0000.0460.3030.0000.3740.0000.0000.3580.000
aspiration_turbo0.1850.3100.2070.3010.2370.3750.2710.3350.2650.5540.3430.3110.1860.3190.3740.3740.9831.0000.0000.0000.0000.0000.0000.0000.0000.0000.1130.0710.0000.0000.0000.0000.1660.0000.0000.0000.0000.0000.1850.0000.0180.0000.0000.0000.0460.3030.0000.3740.0000.0000.3580.000
doornumber_four0.6840.4450.3650.3050.5410.2740.2070.1630.1320.1860.1710.2440.0030.1190.1610.1610.0000.0001.0000.9900.1520.1900.5940.5020.3080.0000.0000.0000.0670.0670.0000.0000.1420.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.1030.0200.0000.0670.1610.0000.0000.1010.000
doornumber_two0.6840.4450.3650.3050.5410.2740.2070.1630.1320.1860.1710.2440.0030.1190.1610.1610.0000.0000.9901.0000.1520.1900.5940.5020.3080.0000.0000.0000.0670.0670.0000.0000.1420.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.1030.0200.0000.0670.1610.0000.0000.1010.000
carbody_convertible0.3320.3630.0000.1850.4220.1510.2530.0710.1340.0000.1240.0000.0000.0000.0000.0000.0000.0000.1520.1521.0000.0000.0640.1140.0000.0000.0950.1170.0710.0710.1230.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.1440.0000.000
carbody_hardtop0.1540.2500.0000.0000.2600.3030.2970.1860.1780.0000.3220.1300.0000.0000.0000.0000.0000.0000.1900.1900.0001.0000.0960.1490.0000.0000.1470.1710.2820.2820.0000.0000.0000.0000.0540.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.000
carbody_hatchback0.4600.4010.4390.1910.5460.2890.2130.3110.2470.1730.1510.0970.1540.1240.1710.1710.0000.0000.5940.5940.0640.0961.0000.6630.2430.0000.1410.1190.0000.0000.0000.0000.0900.0000.0000.0000.1430.0000.0760.0000.0000.0000.0000.1430.1040.0840.1060.1710.0000.1860.1580.000
carbody_sedan0.3970.3330.3210.1970.4940.1820.0750.1340.1360.1760.1590.2000.0950.0940.1540.1540.0000.0000.5020.5020.1140.1490.6631.0000.3280.0000.0000.0000.0230.0230.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0670.0150.0000.0230.1540.0000.0000.0420.000
carbody_wagon0.3150.3130.3120.0000.6820.1710.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.3080.3080.0000.0000.2430.3281.0000.1610.0000.0000.0000.0000.0000.0000.1090.0480.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
drivewheel_4wd0.1710.1680.0350.0000.2020.0000.0000.0880.3940.0000.0000.1940.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1611.0000.2200.1210.0000.0000.0000.0000.0000.0800.3450.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.000
drivewheel_fwd0.3240.5150.5530.5450.4480.6430.6080.5780.2440.1750.5610.2600.5390.6110.0230.0230.1130.1130.0000.0000.0950.1470.1410.0000.0000.2201.0000.9010.0770.0770.1780.0000.2230.4340.0000.1520.1120.1390.0000.4090.3180.0000.0000.1120.1650.3950.0770.0230.0000.4230.0820.000
drivewheel_rwd0.3310.5770.5700.5860.4650.6470.6890.6040.2950.1900.5920.2890.5180.5870.0780.0780.0710.0710.0000.0000.1170.1710.1190.0000.0000.1210.9011.0000.0940.0940.2060.0000.2510.3860.0390.1810.1300.1590.0000.4180.3590.0000.0000.1300.1450.4490.0940.0780.0000.4340.0580.000
enginelocation_front0.2720.5680.0000.1600.2720.1000.6190.3270.6150.0000.8430.4480.1100.1010.0000.0000.0000.0000.0670.0670.0710.2820.0000.0230.0000.0000.0770.0941.0000.8300.0000.0000.0000.1340.3500.0000.0000.0000.0000.1640.2630.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.000
enginelocation_rear0.2720.5680.0000.1600.2720.1000.6190.3270.6150.0000.8430.4480.1100.1010.0000.0000.0000.0000.0670.0670.0710.2820.0000.0230.0000.0000.0770.0940.8301.0000.0000.0000.0000.1340.3500.0000.0000.0000.0000.1640.2630.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.000
enginetype_dohc0.1620.3310.3200.2020.2970.2810.4130.0920.4040.0000.3320.3850.1360.2850.0000.0000.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.1780.2060.0000.0001.0000.0000.0000.3730.0000.0000.0000.0000.0000.1210.2560.0000.0000.0000.0000.1330.0000.0000.0000.2410.0000.000
enginetype_dohcv0.0000.0000.0000.2770.0000.2770.3160.6790.0000.0000.9830.2860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.2050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
enginetype_l0.2760.6570.5390.7210.5270.4530.2380.3330.2650.6480.1010.3570.1900.2960.2230.2230.1660.1660.1420.1420.0000.0000.0900.0000.1090.0000.2230.2510.0000.0000.0000.0001.0000.3730.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.0790.0000.2230.0000.0000.0000.000
enginetype_ohc0.3330.4220.5290.4120.3920.4050.4690.4530.5130.2920.4390.3040.4360.4970.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0480.0800.4340.3860.1340.1340.3730.0000.3731.0000.4270.3920.1750.2090.1020.3780.3260.0000.0000.1750.1020.1940.1340.0000.0000.2840.0800.000
enginetype_ohcf0.1670.3290.2910.1680.2340.1110.2890.4040.8450.0390.4100.2750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0650.3450.0000.0390.3500.3500.0000.0000.0000.4271.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.000
enginetype_ohcv0.0000.3380.2810.3270.2830.4720.7730.3930.0970.2510.5230.2030.4230.5390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1520.1810.0000.0000.0000.0000.0000.3920.0001.0000.0000.4080.0000.4560.3660.0000.1040.0000.0000.1420.0000.0000.0000.2540.0000.000
enginetype_rotor0.3270.0000.0940.1330.6250.1330.7900.3130.1010.0000.0900.5310.1100.4210.0000.0000.0000.0000.1030.1030.0000.0000.1430.0670.0000.0000.1120.1300.0000.0000.0000.0000.0000.1750.0000.0001.0000.0000.0000.2090.0000.0000.0000.8720.0000.0000.7150.0000.0000.0000.0000.000
cylindernumber_eight0.0000.4970.5030.6140.1360.6150.8070.3350.0370.1250.5290.2520.4630.6370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.1590.0000.0000.0000.2050.0000.2090.0000.4080.0001.0000.0000.2470.0000.0000.0000.0000.0000.0270.0000.0000.0000.1220.0000.000
cylindernumber_five0.0000.2750.3120.5040.0000.2870.3420.2300.3200.3330.3960.2950.2270.3000.1630.1630.1850.1850.0000.0000.0000.0000.0760.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0001.0000.4120.0000.0000.0000.0000.0000.1230.0000.1630.0000.0000.0000.000
cylindernumber_four0.2300.4270.4190.5690.3070.5800.8000.2260.2930.1630.7070.2520.6920.6990.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.4090.4180.1640.1640.1210.0000.0000.3780.0000.4560.2090.2470.4121.0000.6570.0000.0000.2090.0750.3260.1640.0000.0000.3560.0510.000
cylindernumber_six0.2030.4490.2790.3500.2000.4330.7490.2610.3830.0000.6600.2600.4460.4150.0000.0000.0180.0180.0000.0000.0000.0000.0000.0000.0000.0000.3180.3590.2630.2630.2560.0000.0000.3260.0000.3660.0000.0000.0000.6571.0000.0000.0000.0000.0000.2250.0000.0000.0000.3440.0000.000
cylindernumber_three0.0450.2770.5360.9800.0000.6750.3520.2860.0000.0000.0000.0000.6790.6750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
cylindernumber_twelve0.0000.0000.0350.3450.5360.4510.5410.0000.3080.9930.9830.0000.2000.3080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
cylindernumber_two0.3270.0000.0940.1330.6250.1330.7900.3130.1010.0000.0900.5310.1100.4210.0000.0000.0000.0000.1030.1030.0000.0000.1430.0670.0000.0000.1120.1300.0000.0000.0000.0000.0000.1750.0000.0000.8720.0000.0000.2090.0000.0000.0001.0000.0000.0000.7150.0000.0000.0000.0000.000
fuelsystem_1bbl0.0000.2030.6070.1720.1730.2100.1460.7290.2980.1210.2590.5500.1960.3470.0000.0000.0460.0460.0200.0200.0000.0000.1040.0150.0000.0000.1650.1450.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0750.0000.0000.0000.0001.0000.1230.0000.0000.0000.1850.0000.000
fuelsystem_2bbl0.4200.4470.5810.5620.3470.6170.4710.4400.4040.2520.6670.3260.6060.6400.1970.1970.3030.3030.0000.0000.0550.0000.0840.0000.0000.0420.3950.4490.0000.0000.1330.0000.0790.1940.0810.1420.0000.0270.1230.3260.2250.0000.0000.0000.1231.0000.0000.1970.0000.6210.1000.000
fuelsystem_4bbl0.2720.0000.0000.0440.5300.0440.6750.2520.0000.0000.0830.4480.1100.3470.0000.0000.0000.0000.0670.0670.0000.0000.1060.0230.0000.0000.0770.0940.0000.0000.0000.0000.0000.1340.0000.0000.7150.0000.0000.1640.0000.0000.0000.7150.0000.0001.0000.0000.0000.0140.0000.000
fuelsystem_idi0.2170.3410.1100.2330.2770.3050.1570.1680.3750.9930.2190.5940.3890.3360.9720.9720.3740.3740.1610.1610.0000.0000.1710.1540.0000.0000.0230.0780.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.1970.0001.0000.0000.2780.0000.000
fuelsystem_mfi0.0890.0000.0000.0000.0000.0000.0000.0000.3080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
fuelsystem_mpfi0.2830.5300.5690.5110.3730.5810.5050.4620.2610.2970.6640.2050.6590.6330.2780.2780.0000.0000.0000.0000.1440.0610.1860.0000.0000.0000.4230.4340.0590.0590.2410.0000.0000.2840.0000.2540.0000.1220.0000.3560.3440.0000.0000.0000.1850.6210.0140.2780.0001.0000.1590.000
fuelsystem_spdi0.2490.1120.1670.1900.2880.1610.0860.0000.4640.3210.2330.1680.0850.1190.0000.0000.3580.3580.1010.1010.0000.0000.1580.0420.0000.0000.0820.0580.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.1000.0000.0000.0000.1591.0000.000
fuelsystem_spfi0.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-08-29T23:37:53.258887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-29T23:37:53.630970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingwheelbasecarlengthcarwidthcarheightcurbweightenginesizeboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgfueltype_dieselfueltype_gasaspiration_stdaspiration_turbodoornumber_fourdoornumber_twocarbody_convertiblecarbody_hardtopcarbody_hatchbackcarbody_sedancarbody_wagondrivewheel_4wddrivewheel_fwddrivewheel_rwdenginelocation_frontenginelocation_rearenginetype_dohcenginetype_dohcvenginetype_lenginetype_ohcenginetype_ohcfenginetype_ohcvenginetype_rotorcylindernumber_eightcylindernumber_fivecylindernumber_fourcylindernumber_sixcylindernumber_threecylindernumber_twelvecylindernumber_twofuelsystem_1bblfuelsystem_2bblfuelsystem_4bblfuelsystem_idifuelsystem_mfifuelsystem_mpfifuelsystem_spdifuelsystem_spfi
0388.6168.864.148.825481303.472.689.011150002127FalseTrueTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
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2194.5171.265.552.428231522.683.479.015450001926FalseTrueTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
3299.8176.666.254.323371093.193.4010.010255002430FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
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9099.5178.267.952.030531313.133.407.016055001622FalseTrueFalseTrueFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
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200-1109.1188.868.955.529521413.783.159.511454002328FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
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Duplicate rows

Most frequently occurring

symbolingwheelbasecarlengthcarwidthcarheightcurbweightenginesizeboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgfueltype_dieselfueltype_gasaspiration_stdaspiration_turbodoornumber_fourdoornumber_twocarbody_convertiblecarbody_hardtopcarbody_hatchbackcarbody_sedancarbody_wagondrivewheel_4wddrivewheel_fwddrivewheel_rwdenginelocation_frontenginelocation_rearenginetype_dohcenginetype_dohcvenginetype_lenginetype_ohcenginetype_ohcfenginetype_ohcvenginetype_rotorcylindernumber_eightcylindernumber_fivecylindernumber_fourcylindernumber_sixcylindernumber_threecylindernumber_twelvecylindernumber_twofuelsystem_1bblfuelsystem_2bblfuelsystem_4bblfuelsystem_idifuelsystem_mfifuelsystem_mpfifuelsystem_spdifuelsystem_spfi# duplicates
0098.8177.866.555.524101223.393.3908.68448002632FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
10107.9186.768.456.732521523.703.52021.09541502833TrueFalseFalseTrueTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse2
20113.0199.669.652.840662583.634.1708.117647501519FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse2
3193.7157.363.850.61967902.973.2309.46855003138FalseTrueTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
4193.7157.363.850.61989902.973.2309.46855003138FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
5198.8177.866.553.723851223.393.3908.68448002632FalseTrueTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse2
6388.6168.864.148.825481303.472.6809.011150002127FalseTrueTrueFalseFalseTrueTrueFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse2
7389.5168.965.051.627561943.742.9009.520759001725FalseTrueTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse2
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